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175
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AI
2011
Springer
14 years 10 months ago
State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning
Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of ...
Daniel Bryce, William Cushing, Subbarao Kambhampat...
134
Voted
SIGCOMM
2006
ACM
15 years 9 months ago
Beyond bloom filters: from approximate membership checks to approximate state machines
Many networking applications require fast state lookups in a concurrent state machine, which tracks the state of a large number of flows simultaneously. We consider the question ...
Flavio Bonomi, Michael Mitzenmacher, Rina Panigrah...
122
Voted
ECML
2004
Springer
15 years 9 months ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
157
Voted
CORR
2010
Springer
204views Education» more  CORR 2010»
15 years 2 months ago
Predictive State Temporal Difference Learning
We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications...
Byron Boots, Geoffrey J. Gordon
135
Voted
APGV
2010
ACM
236views Visualization» more  APGV 2010»
15 years 7 months ago
The effect of stereo and context on memory and awareness states in immersive virtual environments
Spatial awareness is crucial for human performance efficiency of any task that entails perception of space. Memory of spaces is an imperfect reflection of the cognitive activity (...
Adam Bennett, Matthew Coxon, Katerina Mania